HOW GPUS SPEED THE ANALYSIS OF REAL-TIME RISK, FRAUD DETECTION & TRADER SURVEILLANCE. James Mesney Solution Engineer, UK

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1 HOW GPUS SPEED THE ANALYSIS OF REAL-TIME RISK, FRAUD DETECTION & TRADER SURVEILLANCE James Mesney Solution Engineer, UK

Financial Services enterprises must reinvent their business by transforming the core Resilience, Reorientation, Renewal Source: McKinsey

2 FINANCIAL SERVICES: AN INDUSTRY IN TRANSITION Three formidable forces a weak global economy, digitization, and regulation threaten to significantly lower profits by as much as $90B for the global banking industry over the next three years. Financial Services enterprises must reinvent their business by transforming the core Resilience, Reorientation, Renewal Source: McKinsey & Co, PwC Kinetica for: Resilience Manage Revenue, Costs, Capital, and Risk Reorient Customer-centricity, Digitization, Open Bank 2 Renewal - New Markets, Products, Customers

3 In the new world, it is not the big fish which eats the small fish, it s the fast fish which eats the slow fish. Klaus Schwab, Founder and Executive Chairman of the World Economic Forum 2018

4 CORE REQUIREMENTS Breakthrough Speed Linear Scalability Fast, Streaming Ingest Instant Analysis of Streaming Data Natural Language Processing Geospatial Analytics Exceptional Visual Insights BI tool integration ODBC/JDBC support SQL-92 Support Streamlined ML Choice of ML Framework Choice of programming languages Model Lineage Best-in-Class Partner Ecosystem Deploy anywhere

5 USE CASES IN FINANCIAL SERVICES

6 FINANCIAL SERVICES USE CASES Real Time Risk Management Fraud Detection & Cyber Security Trader Surveillance

7 REAL TIME RISK

8 VALUE AT RISK KINETICA S XVA DEPLOYMENT AT MAJOR GLOBAL BANK BUSINESS OBJECTIVE Move counterparty risk analysis from batch/overnight to a streaming/real-time system for flexible real-time monitoring by traders, auditors, and management NEW CAPABILITIES Flexible, fast on-demand reporting of real-time data across hundreds of variables Bonus: Historic analyses of detailed data (not aggregates) enabling multiple new applications, machine learning

Regional Positions ACCOUNTABILITY VaR Limits Export PRE-TRADE What-if Scenarios UDFs IN-DATABASE PROCESSING Custom Connectors RISK

9 BATCH & STREAMING DATA REAL-TIME RISK ARCHITECTURE Live Trading data Counterparties Options Currencies Futures Market Foreign Exchange Bloomberg Reuters INGESTION PARALLEL INGEST & TRANSFORM ON DEMAND SCALE OUT + Native APIs SQL RISK APPLICATIONS MONITORING Global Positions Regional Positions ACCOUNTABILITY VaR Limits Export PRE-TRADE What-if Scenarios UDFs IN-DATABASE PROCESSING Custom Connectors RISK MANAGEMENT P&L, Commissions Sensitivities Liquidity Risk Counterparty Risk SQL PYTHON JAVA C++ CUDA BIDMach ML Libs SIMULATIONS XVA COMPUTATIONS MODELS

10 BENEFITS OF KINETICA S APPROACH More efficient: Pure relational database approach = no more file-based processing Your input data are tables in database easy data engineering Your output data are tables in database easy for users to consume Faster Memory-first GPU-accelerated Deploy anywhere and scale linearly Run on commodity hardware on-premise, or AWS, Azure, GCE Data and workload automatically sharded across n nodes Governance Built-in Security, RBAC, Data encryption, Logging

11 FRAUD DETECTION

KINETICA S LOCATION-BASED FRAUD DETECTION BUSINESS OBJECTIVE Detect fraud and anomalies based on geolocation and time trends in high-volume data Identify broad range of malicious activity in

12 KINETICA S LOCATION-BASED FRAUD DETECTION BUSINESS OBJECTIVE Detect fraud and anomalies based on geolocation and time trends in high-volume data Identify broad range of malicious activity in real-time with confidence to protect against current and emerging threats NEW CAPABILITIES DELIVERED Leverage Kinetica geo-fencing capabilities to identify fraud within, outside, and across user-defined areas. Utilise algorithms and NLP for entity recognition / resolution, and outlier detection 1212

13 FRAUD DETECTION INFRASTRUCTURE DATA Network, comms STREAM PROCESSING USER APPLICATIONS FINANCIAL DATA Tick, Transactional Location PARALLEL INGEST ON DEMAND SCALE OUT + Native API ANALYTICS NEW DATA Social Media, Forums, Chat, Blogs SCRAPE FROM BLOGS, QUARTERLY REPORTS, FORUMS AND MORE Kinetica Connectors NLP & SENTIMENT ANALYSIS TO CLASSIFY TEXT AND TRIGGER DECISIONS UDFs SQL KEYWORDS JOINED WITH TICK DATA AND SENT TO ML/DL ALGORITHMS TO CALCULATE RISK SCORES ON DEMAND. SQL QUERY BIDMach ML Libs OUTLIER DETECTION

14 TRADER SURVEILLANCE

Use AI to reduce false positives NEW CAPABILITIES DELIVERED Leverage Kinetica to fuse financial (trading) with nonfinance

15 KINETICA FOR TRADER SURVEILLANCE BUSINESS OBJECTIVE Minimise market abuse by extracting clear signals from the noise and chatter of the markets in real time. Use AI to reduce false positives NEW CAPABILITIES DELIVERED Leverage Kinetica to fuse financial (trading) with nonfinance (communications) data and others Leverage Kinetica NLP capabilities to identify outliers, sentiment, intention, abuse Retention of detailed data, providing record of evidence of intent, and history for model training 1515

16 TRADER SURVEILLANCE INFRASTRUCTURE DATA Network, comms STREAM PROCESSING USER APPLICATIONS FINANCIAL DATA Tick, Transactional Location PARALLEL INGEST ON DEMAND SCALE OUT + Native API ANALYTICS NEW DATA Social Media, Forums, Chat, Blogs Kinetica Connectors SQL SQL QUERY STREAMING ACTIVITY DATA FUSED WITH SCRAPED FROM , CHAT, BLOGS, FORUMS AND MORE NLP & SENTIMENT ANALYSIS TO CLASSIFY TEXT AND TRIGGER DECISIONS UDFs KEYWORDS JOINED WITH TICK DATA AND SENT TO ML/DL ALGORITHMS TO CALCULATE RISK SCORES ON DEMAND. BIDMach ML Libs OUTLIER DETECTION

17 THE INSIGHT ENGINE FOR THE EXTREME DATA ECONOMY THANKS!

Real-time Analytics Powered by GPU-Accelerated Databases. Chris Prendergast and Woody Christy GTC, May 8, 2017

Real-time Analytics Powered by GPU-Accelerated Databases Chris Prendergast and Woody Christy GTC, May 8, 2017 Kinetica Background United States Army Intelligence seeks a means to assess terrorist and other